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相关概念视频

Overview of Secretory Vesicles01:33

Overview of Secretory Vesicles

9.3K
Secretory vesicles, also known as dense core vesicles (DCVs), are membrane-bound vesicles that transport secretory proteins, such as hormones or neurotransmitters. Regulated secretory vesicles transport proteins from the trans-Golgi network to the exterior of the cell. Proteins present in regulated secretory vesicles are required to be rapidly exocytosed in large amounts upon a specific stimulus.
Various proteins regulate the aggregation of molecules inside the secretory vesicles. Chromogranins...
9.3K
Exocrine Glands: Methods of Secretion01:08

Exocrine Glands: Methods of Secretion

5.9K
Exocrine glands are those that release their secretions through ducts. Based on their mode of secretion, they can be classified into merocrine, apocrine, and holocrine.
Merocrine Secretion
Merocrine secretion is the most common type of exocrine secretion. The secretions are enclosed in vesicles and moved to the cell's apical surface, where the contents are released by exocytosis. For example, mucous, a watery secretion rich in the glycoprotein mucin, is a merocrine secretion. The eccrine...
5.9K
Fusion of Secretory Vesicles with the Plasma Membrane01:26

Fusion of Secretory Vesicles with the Plasma Membrane

16.5K
Proteins and neurotransmitters in secretory vesicles can be released from a cell upon vesicle docking, priming, and fusion with the plasma membrane. Vesicles are docked and primed in preparation for the quick exocytosis of their contents in response to a stimulus. The fusion process is mainly carried out by a SNAP Receptor or SNARE complex, consisting of synaptobrevin, syntaxin-1, and SNAP-25.
In 1993, Jim Rothman proposed that the antiparallel pairing of vesicular and transmembrane SNAREs, or...
16.5K
Regulation of Hormone Secretion01:19

Regulation of Hormone Secretion

6.1K
Regulation of hormone secretion is a finely tuned orchestration driven by various types of stimuli, encompassing neural, humoral, and hormonal signals. Environmental cues instigate neural stimuli, where action potentials traverse nerve fibers to reach their designated targets. An illustrative scenario is the body's response to stress, wherein the sympathetic nervous system releases epinephrine from the adrenal glands, inducing the well-known 'fight or flight' reaction.
Humoral...
6.1K

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相关实验视频

Updated: Jan 12, 2026

Investigating Mast Cell Secretory Granules; from Biosynthesis to Exocytosis
16:01

Investigating Mast Cell Secretory Granules; from Biosynthesis to Exocytosis

Published on: January 26, 2015

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TXSelect:一种多任务学习模型,用于识别分泌效应因子.

Jing Li1,2,3, Qing Liu4, Quan Zou2

  • 1Department of Microbiology, University of Hong Kong, Hong Kong, China.

PLoS computational biology
|November 6, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了TXSelect,这是一个计算工具来分类细菌分泌效应器 (TXSE). 该框架准确地识别了多种效应因子类型,有助于理解病原体机制和开发新疗法.

更多相关视频

Monitoring the Effect of Osmotic Stress on Secretory Vesicles and Exocytosis
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Monitoring the Effect of Osmotic Stress on Secretory Vesicles and Exocytosis

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Assessing the Secretory Capacity of Pancreatic Acinar Cells
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Assessing the Secretory Capacity of Pancreatic Acinar Cells

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相关实验视频

Last Updated: Jan 12, 2026

Investigating Mast Cell Secretory Granules; from Biosynthesis to Exocytosis
16:01

Investigating Mast Cell Secretory Granules; from Biosynthesis to Exocytosis

Published on: January 26, 2015

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Monitoring the Effect of Osmotic Stress on Secretory Vesicles and Exocytosis
08:08

Monitoring the Effect of Osmotic Stress on Secretory Vesicles and Exocytosis

Published on: February 19, 2018

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Assessing the Secretory Capacity of Pancreatic Acinar Cells
09:52

Assessing the Secretory Capacity of Pancreatic Acinar Cells

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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 致病微生物利用分泌效应器来操纵宿主过程,影响生存和致病性.
  • 由于序列和结构异质性,对各种细菌效应剂 (I,II,III,IV和VI型分泌效应剂 - - TXSE) 进行准确的分类是具有挑战性的.

研究的目的:

  • 开发一个高效的计算框架,TXSelect,用于同时分类多个TXSE类型.
  • 整合先进的蛋白质特征,以提高分类准确性和生物洞察力.

主要方法:

  • 开发了TXSelect,这是一个多任务学习框架,具有共享的骨干和TXSE分类的任务特定头部.
  • 综合蛋白质嵌入特征来自进化规模建模 (ESM) N-终端平均值与经典描述符:基于距离的残留物 (DR) 和分裂氨基酸组成一般 (SC-PseAAC-General).
  • 通过严格的验证和测试评估特征组合和模型性能,包括统一的多重近似和投影用于可视化.

主要成果:

  • 最佳特征组合 (ESM N-终端平均值 + DR + SC-PseAAC) 实现了高精度,验证F1得分为0.867和测试F1得分为0.8645.
  • 在不同的TXSE类型中,TXSelect展示了强大的泛化能力.
  • 模型的可解释性和区分能力通过全面的评估和可视化技术来验证.

结论:

  • TXSelect提供了一个准确和高效的计算工具来分类细菌TXSE.
  • 该框架支持对病原体与宿主相互作用的更深入的生物学理解.
  • 这种工具在识别传染病治疗点方面具有潜在的应用.